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Outline
With the widespread applications of energy storage plants in new power systems, improving the prediction accuracy of lithium-ion battery state-of-charge (SOC) is of significance for enhancing the safety and stability of energy storage plants and power systems. A battery SOC prediction method is proposed, which combines a multi-scale convolutional neural network (CNN) and a residual long short-term memory (LSTM) network. First, the multi-scale CNN is employed to automatically extract different types of features from the sensor data. Then, the features are fed into an LSTM network to mine the correlation in the time dimension. Meanwhile, residual connections are added at both ends of the LSTM network to avoid model overfitting. The comparative experiments on the measured data of the energy storage system show that the proposed model achieves the best prediction results in terms of all the indicators. In particular, the key features can be extracted better when the battery is under complex operating conditions, resulting in a better prediction performance.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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